Temporally-aware evaluative score

ABSTRACT

A method includes processing a performance query to a dimensional data model by processing dimension coordinates that exist within the dimensional data model, wherein the dimension coordinates have a first particular grain (“finer grain”) that is finer than a second particular grain (“coarser grain”), the method to determine an evaluative score for a particular finer grain value based on performance facts for dimension coordinates associated with the particular finer grain value. Performance parameters are determined relative to a particular coarser grain value, against which to measure the performance facts associated with the finer grain value, including processing the temporal relationships of finer grain values to coarser grain values for the dimension coordinates. The evaluative score is determined for the particular finer grain value based on performance facts of dimension coordinates having the particular finer grain value, in view of the determined performance parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior, co-pending U.S. patentapplication Ser. No. 11/860,275, filed on Sep. 24, 2007, and entitled“TEMPORALLY-AWARE EVALUATIVE SCORE,” which is incorporated herein byreference in its entirety for all purposes.

BACKGROUND

The present invention is in the field of considering the phenomena ofslowly changing dimensions in the process of evaluating facts of orderived from a collection of facts organized as, or otherwise accessibleaccording to, a dimensional data model. For shorthand throughout thisdescription, such a collection of facts is referred to as adimensionally-modeled fact collection.

It is known to respond to a query to a dimensionally-modeled factcollection by reporting on the facts contained in thedimensionally-modeled fact collection. Reports are typically generatedto allow one to glean information from facts that are associated withlocations in a dimensional data space according to which thedimensionally-modeled fact collection is modeled.

Locations in an n-dimensional data space are specified by n-tuples ofcoordinates, where each member of the tuple corresponds to one of the ndimensions. For example, (“San Francisco”, “Sep. 30, 2002”) may specifya location in a two-dimensional data space, where the dimensions areLOCATION and TIME. Coordinates need not be singled-grained entities.That is, coordinates of a single dimension may exist at, or be specifiedwith respect to, various possible grains (levels of detail). In oneexample, a coordinate of a LOCATION dimension is comprised of thefollowing grains: CONTINENT, COUNTRY, CITY.

The order of the grains may have some hierarchical significance. Thegrains are generally ordered such that finer grains are hierarchically“nested” inside coarser grains. Using the LOCATION dimension example,the CITY grain may be finer than the COUNTRY grain, and the COUNTRYgrain may be finer than the CONTINENT grain. Where the order of thegrains of a dimension has hierarchical significance, the value of acoordinate of that dimension, at a particular grain, is nominally suchthat the value of the coordinate of that dimension has only one value atany coarser grain for that dimension. In an example, a value of acoordinate of a LOCATION dimension may be specified at the CITY grain ofthe LOCATION dimension by the value “Los Angeles.” This same coordinatehas only one value at the COUNTRY and CONTINENT grains: “US” and “NORTHAMERICA”, respectively.

There is a well-known phenomenon in the field of dimensional datamodeling of “slowly changing dimensions,” mentioned briefly above. Thisis a phenomenon in which the relationship of grains for a dimension maychange over time. While it may be contrived to consider the concept ofslowly changing dimensions with reference to the example LOCATIONdimension (since, generally, the relationship of CONTINENT, COUNTRY andCITY grains will not change over time), there are other more realisticexamples of this phenomenon.

As one illustration, consider an EMPLOYEE dimension that is intended torepresent an organizational chart of a company. In this example, theEMPLOYEE dimension comprises the following grains: ORGANIZATION,DIVISION, TEAM and PERSON. Using this example, it can be seen thatvalues of coordinates at various grains may change as a person movesfrom one team to another team (or, perhaps, a team moves from onedivision to another division). For example, in one month, Joe worked onthe Red Team; the next month, he worked on the Blue Team. This may bemodeled by one EMPLOYEE dimension coordinate having the value “Joe” atgrain PERSON and the value “Red Team” at grain TEAM, plus a secondEMPLOYEE dimension coordinate also having the value “Joe” and grainPERSON but the value “Blue Team” at grain TEAM. It is also possible toencode in the representation of the dimension coordinates the specifictime intervals during which these grain relationships obtained.

SUMMARY

A method includes processing a performance query to a dimensional datamodel by processing dimension coordinates that exist within thedimensional data model, wherein the dimension coordinates have a firstparticular grain (“finer grain”) that is finer than a second particulargrain (“coarser grain”), the method to determine an evaluative score fora particular finer grain value based on performance facts for dimensioncoordinates associated with the particular finer grain value.

Performance parameters are determined relative to a particular coarsergrain value, against which to measure the performance facts associatedwith the finer grain value, including processing the temporalrelationships of finer grain values to coarser grain values for thedimension coordinates. The evaluative score is determined for theparticular finer grain value based on performance facts of dimensioncoordinates having the particular finer grain value, in view of thedetermined performance parameters.

Processing the temporal relationship of finer grain values to coarsergrain values for the dimension coordinates may include unfettering,disambiguation and processing a temporal mode in the query. Byconsidering the temporal relationships of finer grain values to coarsergrain values for dimension coordinates in the dimensionally-modeled factcollection, the evaluative score may be determined in a manner thatprovides a more accurate evaluation, in light of historical occurrencesrepresented by the dimensionally-modeled fact collection.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a broad aspect of a system todetermine an evaluative score based on performance facts for dimensioncoordinates having a particular finer grain value, in view ofperformance parameters determined relative to a particular coarser grainvalue.

FIG. 2 illustrates an example of considering the temporal relationshipsof finer grain values to coarser grain values for dimension coordinatesin the dimensionally-modeled fact collection includes “disambiguation.”

FIG. 3 illustrates an example of considering the temporal relationshipsof finer grain values to coarser grain values for dimension coordinatesin the dimensionally-modeled fact collection includes “unfettering.”

FIG. 4 graphically illustrates a simple situation in which there is onlyone finer grain value for which there is an ambiguity and, further, theambiguity is between only two possible coarser grain values.

FIG. 5 graphically illustrates an example in which, similar to the FIG.1 example, disambiguation separately occurs with respect todisambiguation time chambers for each time reporting label of the timereporting range of the report query.

FIG. 6 graphically illustrates an example in which a disambiguationoccurs for a disambiguation time chamber that spans more than one timereporting label.

FIG. 7 is a block diagram illustrating an example architecture of asystem in which reporting of facts of a dimensionally-modeled factcollection may be performed, including disambiguating as desired or asotherwise determined to be appropriate.

FIG. 8 is a flowchart illustrating an example of multiple-passprocessing including disambiguation.

FIG. 9 is a block diagram illustrating an example architecture of asystem in which reporting of facts of a dimensionally-modeled factcollection is performed in an unfettered manner.

FIG. 10 is a flowchart illustrating a multiple-pass processing methodrelative to unfettering.

FIGS. 11A and 11B together illustrate an example of generating a reportin an unfettered manner.

FIG. 12 is a block diagram illustrating an example architecture of asystem in which a temporal mode, as part of report query to adimensionally-modeled fact collection, is processed to determine a timeextent descriptor useable to report on the facts of the dimensionallymodeled fact collection.

FIGS. 13 to 16 illustrate how various different temporal modes may beapplied in a simple example, where processing is only in one dimension.

FIG. 17 illustrates an example of a report display screen, includingfunctionality for a report configuration menu to specify, among otherthings, a temporal mode corresponding to the report query.

DETAILED DESCRIPTION

The inventors have realized that it is desirable to consider thephenomenon of slowly changing dimensions in the process of evaluatingfacts of a collection of facts organized as, or otherwise accessibleaccording to, a dimensionally-modeled fact collection. This may bedesirable as to what facts are considered to represent the performancebeing evaluated and/or as to what facts are considered in determiningthe performance parameters, against which the performance is beingevaluated.

More particularly, in accordance with an aspect, an evaluation query toa dimensionally modeled fact collection is handled by processingdimension coordinates that exist within the dimensionally-modeled factcollection, wherein the dimension coordinates have a first particulargrain (“finer grain”) that is finer than a second particular grain(“coarser grain”). An evaluative score is determined for a particularfiner grain value based on performance facts for dimension coordinatesassociated with the particular finer grain value. The evaluative scoreis determined based on performance facts for dimension coordinateshaving the particular finer grain value, in view of performanceparameters determined relative to a particular coarser grain value.

More generally, processing an evaluative query to determine performancefacts and/or performance parameters, on which determination of theevaluative score is based, includes processing the temporalrelationships of finer grain values to coarser grain values for thedimension coordinates.

FIG. 1 illustrates an example in accordance with a broad aspect. Anevaluative query 102 includes two portions, a performance fact portion104 and a performance parameter portion 106. The performance factportion 104 and/or the performance parameter portion 106 are processedin consideration of temporal relationships of finger grain values tocoarser grain values for dimension coordinates in thedimensionally-modeled fact collection 110. More detailed examples ofsuch processing are discussed later.

The resulting performance facts 112 and performance parameters 114 areprocessed (116) to determine an evaluative measure 118. The processingmay include, for example, processing the performance facts 112 togenerate a statistic and then to compare that statistic to a determinedperformance parameter. It is contemplated that the processing of theperformance facts 112 may include other more sophisticated comparisonsand/or other processing.

By considering the temporal relationships of finer grain values tocoarser grain values for dimension coordinates in thedimensionally-modeled fact collection, the evaluative score may bedetermined in a manner that provides a more accurate evaluation, inlight of historical occurrences represented by the dimensionally-modeledfact collection.

We now turn to FIG. 2, which illustrates an example of considering thetemporal relationships of finer grain values to coarser grain values fordimension coordinates in the dimensionally-modeled fact collection 110.In particular, FIG. 2 illustrates that an example of such considerationincludes disambiguation similar to that described in U.S. patentapplication Ser. No. 11/615,694 (“the '694 application”), filed underAttorney Docket Number MRCDP003 on Dec. 22, 2006 and assigned to theassignee of the present application. The '694 application isincorporated by reference herein in its entirety.

More specifically, FIG. 2 illustrates that disambiguation 202 mayinclude, for example, remapping the correspondence of fine-grainedentities to coarser-grained entities, of dimension coordinates resultingfrom the performance parameter portion 106 of the evaluative query 102,such that each fine-grained entity maps to only one of thecoarser-grained entities. For example, the evaluative query may be tocompare the cookie-eating performance of a particular person (i.e., asindicated by metric values of dimension coordinates having a value atthe Person grain corresponding to that particular person) as compared tothe average cookie-eating performance for the Blue Team.

The determination of average cookie-eating performance for the Blue Teammay be subject to disambiguation, as is discussed in an example relativeto FIG. 1 of the '694 application. The example refers to “Bill,” who wasa member of two different teams during a relevant time period (in theparticular example, during Q4-2005). More particularly, during Q4-2005,Bill ate 60 cookies while on Red Team, and Bill also ate 60 cookieswhile on Blue Team. According to the discussion in the '694 applicationof the example, the ambiguity about Bill's team membership duringQ4-2005 can be arbitrarily disambiguated. The '694 application goes onto discuss, at length, various aspects of disambiguation.

That discussion is provided in the section below, entitled“Disambiguation Disclosure.” Only figure numbers (including referencenumerals) and table numbers have been modified, in order to notduplicate figure numbers and table numbers used elsewhere in theapplication.

We now turn to FIG. 3, which illustrates another example of consideringthe temporal relationships of finer grain values to coarser grain valuesfor dimension coordinates in the dimensionally-modeled fact collection110. In particular, FIG. 3 illustrates that an example of suchconsideration includes “unfettering” similar to that described in U.S.patent application Ser. No. 11/552,394 (“the '394 application”), filedunder Attorney Docket Number MRCDP002 on Oct. 24, 2006 and assigned tothe assignee of the present application. The '394 application isincorporated by reference herein in its entirety. More specifically,FIG. 3 illustrates that unfettering 302 may be, for example, by (atleast conceptually) re-expressing the constraint in the performance factportion 104 of the evaluative query 102 in terms of finer-grainedentities of dimension coordinates satisfying an original,coarser-grained constraint.

For example, an original, coarser-grained constraint at the TEAM grainmay be in terms of “Blue Team,” whereas there may be a plurality ofdimension coordinates having a value at a finer grain (PERSON grain) of“Bill,” but only some of which also have a value of “Blue Team” at theTEAM grain. This is similar to an example in the '394 application, wherea constraint expressed in terms of “Blue Team” was re-expressed in termsof “Bill” (i.e., at the finer, PERSON grain) because Bill was on theBlue Team. This resulted in including facts for Bill while Bill was onanother team, such as Red Team.

The '394 application goes on to discuss, at length, various aspects ofunfettering. That discussion is provided below, in the section entitled“Unfettering Disclosure.” Only figure numbers (including referencenumerals) and table numbers have been modified, in order to notduplicate figure numbers and table numbers used elsewhere in theapplication.

In addition, it is noted that the evaluative query 102 itself mayinclude one or more temporal modes (e.g., one temporal modecorresponding to the performance fact portion 104 of the evaluativequery 102 and another temporal mode corresponding to the performanceparameter portion 106 of the evaluative query 102). A temporal mode maybe processed in a manner similar to that described in U.S. patentapplication Ser. No. 11/427,718 (“the '718 application”), filed underAttorney Docket Number MRCDP001 on Jun. 29, 2006 and assigned to theassignee of the present application. The '718 application isincorporated by reference herein in its entirety.

Thus, for example, each temporal mode may be processed to determine atime extent descriptor. The processing of the specified temporal modemay be in view of the dimension coordinate constraints of the evaluativequery and/or a context. A fact collection query is generated, and aresult of providing the fact collection query to thedimensionally-modeled fact collection is processed. The processed resultincludes an indication of dimensional values as appropriate in view ofthe time information from the time extent descriptor. More particularly,the time extent descriptor includes information about a period of time(i.e., from a “starting time” to an “ending time”) to utilize indetermining which values at one grain of a dimension should beconsidered to be also present at another grain (e.g., a coarser grain)of that dimension.

For example, the query may include the constraint of number of cookieseaten (performance facts) for “all members of Blue Team.” Given thetemporality of the team grain (i.e., people may move from team to team),a temporal mode (from which a time extent descriptor may be determined)may be processed (e.g., in accordance with the teachings of the '718application) to determine which PERSON grain values to associate withthe TEAM grain value of “Blue Team.”

That discussion is below in the section entitled “Temporal ModeSpecification Disclosure.” Only figure numbers (including referencenumerals) and table numbers have been modified, in order to notduplicate figure numbers and table numbers used elsewhere in theapplication.

DISAMBIGUATION DISCLOSURE

The inventors have realized that it is desirable to consider thephenomenon in which, for a subset of a plurality of dimensioncoordinates that satisfy a report query, there are dimension coordinatesof the subset that have the same grain value at a finer grain but adifferent grain value at a coarser grain. In this case, when performingoperations with respect to dimension coordinates of this subset, thereis ambiguity as to what coarser grain value to associate with the finergrain value.

This phenomenon may arise, for example, when one or more dimensions inwhich the dimension coordinates exist is a slowly changing dimension.This is a phenomenon in which the relationship of grains for a dimensionmay change over time. While it may be contrived to consider the conceptof slowly changing dimensions with reference to the example LOCATIONdimension (since, generally, the relationship of CONTINENT, COUNTRY andCITY grains will not change over time), there are other more realisticexamples of this phenomenon.

As one illustration, consider an EMPLOYEE dimension that is intended torepresent an organizational chart of a company. In this example, theEMPLOYEE dimension comprises the following grains: ORGANIZATION,DIVISION, TEAM and PERSON. Using this example, it can be seen thatvalues of coordinates at various grains may change as a person movesfrom one team to another team (or, perhaps, a team moves from onedivision to another division). For example, at the beginning of onequarter, Bill worked on the Red Team; sometime during the quarter, Billmoved to the Blue Team. This may be modeled by one EMPLOYEE dimensioncoordinate having the value “Bill” at grain PERSON and the value “RedTeam” at grain TEAM, plus a second EMPLOYEE dimension coordinate alsohaving the value “Bill” at grain PERSON but the value “Blue Team” atgrain TEAM. It is also possible to encode in the representation of thedimension coordinates the specific time intervals during which thesegrain relationships obtained.

As a simplistic example of an operation to be performed with respect todimension coordinates satisfying a dimension coordinate constraint, itmay be desired to compute the average number of cookies eaten by eachteam's members during Q4 2005. This computation considers multipledimensional grains. That is, the statistical population is defined atthe PERSON grain (cookies eaten by members), while the reported resultis at the TEAM grain (i.e., the results are reported on a per teambasis) for the time period corresponding to the Q4 2005 time reportinglabel (shorthand—“Q4 2005 time period”).

Consider the following dimension coordinates, and metric values,characterized by a time period corresponding to the Q4 2005 time period:

TABLE 1 Metric Value (# Person Dimension Coordinate cookies) TimeReporting Label Mary: Red Team 100 Q4-2005 Bill: Red Team 60 Q4-2005Bill: Blue Team 60 Q4-2005 Saul: Blue Team 90 Q4-2005

The cookie eating metric values could be left attached to both thePERSONs and TEAMs to which they accrued, and an average could becomputed as:

(Result 1-1)

Red Team=(100+60)/2=80

Blue Team=(60+90)/2=75

This preserves an ambiguity about Bill's team membership during the Q42005 time period and artificially deflates the per PERSON average ofboth teams, since Bill is counted twice.

On the other hand, the ambiguity about Bill's team membership during theQ4 2005 time period can be arbitrarily disambiguated. For example, allof Bill's cookie eating metric values for the Q4 2005 time period couldbe attributed to the Red Team, even metric values for cookies eaten byBill while Bill was on the Blue Team:

(Result 1-2)

Red Team=(100+(60+60))/2=110

Blue Team=(90)/1=90

Or, all of Bill's cookie eating metric values could be attributed to theBlue Team for the Q4 2005 time period, even metric values for cookieseaten by Bill while Bill was on the Red Team:

(Result 1-3)

Red Team=(100)/1=100

Blue Team=((60+60)+90)/2=105

In accordance with an aspect of the invention, then, and referring tothe specific example of Bill and the Red Team and Blue Team, adetermination is made whether those dimension coordinates correspondingto the Q4 2005 time reporting label and having a value of Bill at thePERSON grain are treated as having a value of Red Team or of Blue Teamat the TEAM grain. Thus, for example, if it is determined that dimensioncoordinates having a value of Bill at the PERSON grain are to be treatedas having a value of Red Team at the TEAM grain, then even the dimensioncoordinate having a value of Bill at the PERSON grain and having a valueof Blue Team at the TEAM grain will be treated as having a value of RedTeam at the TEAM grain.

More generally, there may be a subset of a plurality of dimensioncoordinates satisfying a dimension coordinate constraint of a reportquery, where each dimension coordinate of the subset is such that thereis at least one other dimension coordinate of the subset having a finergrain value that is the same as the finer grain value of that dimensioncoordinate (e.g., Bill at the PERSON grain) and the at least one otherdimension coordinate also has a coarser grain value that is differentfrom the coarser grain value of that dimension coordinate (e.g., anotherdimension coordinate has a value of Red Team at the TEAM grain and thatdimension coordinate has a value of Blue Team at the TEAM grain). Inaccordance with the aspect, for every unique finer grain value of thedimension coordinates of the subset (e.g., Bill is a unique grain valueat the PERSON grain), the coarser grain value to associate with alldimension coordinates of the subset having that finer grain value isconsidered to be the coarser grain value of one of the dimensioncoordinates, of the subset, having that finer grain value (e.g., thecoarser grain value to associate with the finer grain value of Bill isconsidered to be either Red Team or Blue Team).

FIG. 4 illustrates this aspect graphically. With respect to FIG. 4, thePERSON grain is the finer grain and the TEAM grain is the coarser grain.The dimension coordinate 402 and the dimension coordinate 404 areconsidered to be dimension coordinates of a “subset.” (As mentionedabove, each dimension coordinate of a subset is such that there is atleast one other dimension coordinate of the subset having a finer grainvalue that is the same as the finer grain value of that dimensioncoordinate and the at least one other dimension coordinate also has acoarser grain value that is different from the coarser grain value ofthat dimension coordinate.) More particularly, the dimension coordinate402 and the dimension coordinate 404 each have the value Bill at thePERSON grain (finer grain), but the dimension coordinate 402 and thedimension coordinate 404 have different values at the TEAM grain. Thatis, the dimension coordinate 402 has the value Blue Team at the TEAMgrain, and the dimension coordinate 404 has the value Red Team at theTEAM grain.

Some mechanism has been used to determine and process the time period bywhich the dimension coordinates 402 and 404 are characterized and, thus,to associate each of the dimension coordinates 402 and 404 (and,perhaps, one or more dimension coordinates that are not shown, for whichthere is no ambiguity as to what coarser grain value to associate withthe finer grain values) with a particular time reporting label. In theFIG. 4 examples, the (one and only) particular time reporting label isQ4 2005.

There are various mechanisms by which dimension coordinates may beassociated with time reporting labels One example is described inpending U.S. patent application Ser. No. 11/427,718, entitled “TemporalExtent Considerations in Reporting on Facts Organized as aDimensionally-Modeled Fact Collection,” filed on Jun. 29, 2006 andincorporated by reference herein in its entirety for all purposes. Forexample, in the U.S. patent application Ser. No. 11/427,718, thefollowing description is provided:

-   -   In one example, the multidimensional fact collection includes        metadata that provides information from which the temporal        characteristics of the grain relationships can be discerned.        (See, for example, the article entitled “Kimball Design Tip #8:        Perfectly Partitioning History With The Type 2 Slowly Changing        Dimension,” available at        http://www.kimballgroup.com/html/designtipsPDF/DesignTips2000%20/KimballDT8Perfectly.pdf,        which describes augmenting dimension records with “time stamps”        to temporally characterize the dimension records.)        For purposes of the present discussion, however, it should just        be considered that a particular association of dimension        coordinates to time reporting label(s) has been or can be        somehow determined.

Referring still to FIG. 4, block 406 represents the coarser grain valueof Blue Team, whereas block 408 represents the coarser grain value ofRed Team. It can be seen that Blue Team and Red Team are each a possiblecoarser grain value to associate with the finer grain value of Bill,which is the finer grain value at the PERSON grain of both the dimensioncoordinate 402 and the dimension coordinate 404. In the FIG. 4 diagram,the “switch” 410 graphically represents a result of a disambiguationdetermination 412 as to which of the Blue Team value and the Red Teamvalue is to be associated with the finer grain value of Bill, at thePERSON grain.

For example, if the result of the disambiguation determination 412 isthat the Blue Team value is to be associated with the value Bill at thePERSON grain, then the switch 410 is figuratively positioned such thatthe Blue Team value 106 is associated with the value of Bill at thePERSON grain for the dimension coordinate 402 and the dimensioncoordinate 404, even though the dimension coordinate 404 has an actualvalue of Red Team at the TEAM grain. Referring to the examplesabove—computing the average number of cookies eaten by each team'smembers for the Q4 2005 time period—this would result in processing thedimension coordinates as set forth with respect to Result 1-3 above.

On the other hand, if the result of the disambiguation determination 412is that the Red Team value is to be associated with the value Bill atthe PERSON grain, then the switch 410 is figuratively positioned suchthat the Red Team value is associated with the value of Bill at thePERSON grain for both the dimension coordinate 402 and the dimensioncoordinate 404, even though the dimension coordinate 402 has an actualvalue of Blue Team at the TEAM grain. Again referring to the examplesabove—computing the average number of cookies eaten by each team'smembers during Q4 2005—this would result in processing the dimensioncoordinates as set forth with respect to Result 1-2 above.

It is noted that FIG. 4 represents a simple situation in which, for aparticular subset of dimension coordinates, there is only one finergrain value for which there is an ambiguity as to an associated coarsergrain value and, further, the ambiguity is between only two possiblecoarser grain values. By extension, there may be situations in whichthere is more than one finer grain value for which there is anambiguity. In general, for example, these situations may be handled byseparately disambiguating for each finer grain value for which there isan ambiguity. Furthermore, an ambiguity may be between more than twopossible coarser grain values. Where an ambiguity is between more thantwo possible coarser grain values, the disambiguation results in asingle one of the possible coarser grain values being associated with aparticular finer grain value.

As mentioned several time above, we may collectively denote thedimension coordinates having finer grain values for which there is anambiguity as a “subset” of dimension coordinates. Furthermore, FIG. 4,along with Table 1 and Results 1-2 and 1-3, illustrates an example wherenot only the dimension coordinates of the subset, but also the dimensioncoordinates of the larger group to which the subset belongs, have allbeen determined to be associated with a single particular time period.In particular, the example is one in which each of the dimensioncoordinates considered for disambiguation corresponds to the time periodof the single Q4 2005 time reporting label.

Turning now to FIG. 5, unlike the FIG. 4 example, FIG. 5 exhibits anexample for which there is more than a single time reporting label. Thatis, with respect to FIG. 5, a report is on the average number of cookieseaten by each team's members during Q4 2005, the time reporting range,reported on a monthly basis. The number of time reporting labels isthree—OCT-2005, NOV-2005 and DEC-2005.

According to this example, each time reporting label corresponds to aseparate non-overlapping time period, namely, the time periodsassociated with the OCT-2005, NOV-2005 and DEC-2005 time periods. Inaddition, each dimension coordinate satisfying the dimension coordinateconstraint of a report query is associated with one of the separatenon-overlapping time periods which we call “disambiguation timechambers.” Each disambiguation time chamber corresponds to a differentnon-overlapping time period of the time reporting range, and the subsetsfor which there is disambiguation exist on a disambiguation time chamberby disambiguation time chamber basis, based on a correspondence betweena time period with which a dimension coordinate is associated and a timeperiod associated with a disambiguation time chamber.

FIG. 5 illustrates a simple example, in which the disambiguation timechambers are at the same resolution as the time reporting labels and,thus, the disambiguation time chambers coincide with the time reportinglabels. Since the disambiguation time chambers coincide with timereporting labels in the FIG. 5 example, not only do the subsets exist ona disambiguation time chamber by disambiguation time chamber basis, italso follows that the subsets exist on a time reporting label by timereporting label basis. In the FIG. 5 example, there may be a subset forwhich there is disambiguation for each of the OCT-2005, NOV-2005 andDEC-2005 time periods. By contrast, we explain later with respect to theFIG. 6 example how the disambiguation time chambers may be at a coarserresolution than the time reporting labels and, thus, each disambiguationtime chamber may simultaneously correspond to two or more time reportinglabels. (We also note that a particular dimension coordinate may beassociated with more than one of the time periods with which timereporting labels are associated. We will note an example of this withreference to Table 2, later in this description.)

Perhaps an easier way to consider this concept is that a time period towhich each separate set of dimension coordinates corresponds is definedby a time period to which one or more of the time reporting labelscorresponds. For shorthand, we refer to the time period to which one ofthe separate sets of dimension coordinates corresponds as a“disambiguation time chamber.” In the FIG. 4 example, there is onedisambiguation time chamber, and it corresponds to the Q4 2005 timeperiod. In the FIG. 5 example, there are three disambiguation timechambers, and the three disambiguation time chambers correspond to theOCT-2005, NOV-2005 and DEC-2005 time periods, respectively. Later, wewill see that not only may a disambiguation time chamber be defined bythe time period to which one of the time reporting labels correspondsbut, also, a disambiguation time chamber may be defined by the timeperiod to which more than one of the time reporting labels collectivelycorrespond (or, put another way, a disambiguation time chamber maycorrespond to one or more time reporting labels).

Before leaving FIG. 5, we again mention that, as discussed aboverelative to FIG. 4, for each subset of dimension coordinates consideredfor disambiguation, there may be one or more finer grain values forwhich there is an ambiguity as to what is the associated coarser grainvalue. For example, maybe there is only an ambiguity as to the coarsergrain value associated with “Bill” or maybe there is an ambiguity as tothe coarser grain value associated with “Bill” and there is also anambiguity as to the coarser grain value associated with “Steve.”Furthermore, for a particular one of those finer grain values, thedisambiguation may be among two or more coarser grain values (e.g., thedisambiguation may be among Red Team and Blue team, or thedisambiguation may be Red Team, Blue Team and Green Team).

We now discuss an example in which a situation like the FIG. 5 situationmay apply. That is, we discuss an example in which there are multipledisambiguation time chambers, the disambiguation time chambers being atthe same resolution as the time reporting labels such that eachdisambiguation time chamber corresponds to one separate time reportinglabel. Consider the following dimension coordinates, metric values andtime reporting labels:

TABLE 2 Metric Value (# Person Dimension Coordinate cookies) TimeReporting Label Mary: Red Team 25 OCTOBER 2005 Mary: Red Team 35NOVEMBER 2005 Mary: Red Team 40 DECEMBER 2005 Bill: Red Team 40 OCTOBER2005 Bill: Red Team 20 NOVEMBER 2005 Bill: Blue Team 20 NOVEMBER 2005Bill: Blue Team 40 DECEMBER 2005 Saul: Blue Team 30 OCTOBER 2005 Saul:Blue Team 30 NOVEMBER 2005 Saul: Blue Team 30 DECEMBER 2005(Above, it was mentioned that an example would be discussed, withreference to Table 2, of a particular dimension coordinate beingassociated with more than one of the time periods to which timereporting labels correspond. In Table 2, an example of such a dimensioncoordinate includes the dimension coordinate having the value Mary atthe PERSON grain and having the value Red Team at the TEAM grain. Thisdimension coordinate is associated with all of the following timereporting labels: OCT 2005, NOV 2005 and DEC 2005.)

With respect to the Table 2 dimension coordinates, metric values andtime reporting labels, the cookie eating metric values could be left“attached” to both the PERSONs and TEAMs to which it accrued (i.e., nodisambiguation), and an average per team, per each time reporting label,could be computed as:

(Result 2-1) Month Red Team Blue Team OCTOBER 2005 (25 + 40)/2 = 32.5(30)/1 = 30 NOVEMBER 2005 (35 + 20)/2 = 27.5 (20 + 30)/2 = 25 DECEMBER2005 (40)/1 = 40 (40 + 30)/2 = 35As with Result 1-1 above, it is noted how the per-PERSON average isartificially depressed for both TEAMs for the time reporting labelNOV-2005, corresponding to the month Bill changed teams.

Alternatively, for a disambiguation time chamber defined by the timeperiod to which the NOV-2005 time reporting label corresponds, the TEAMvalue of Red Team could be attributed to Bill for the NOV-2005 timereporting label. (It is noted that, with respect to the dimensioncoordinates in Table 2, dimension coordinates associated with theNOV-2005 disambiguation time chamber are the only dimension coordinatesfor which an ambiguity exists as to coarser grain values associated withparticular finer grain values.)

(Result 2-2) Month Red Team Blue Team OCTOBER 2005 (25 + 40)/2 = 32.5(30)/1 = 30 NOVEMBER 2005 (35 + (20 + 20))/2 = 37.5 (30)/1 = 30 DECEMBER2005 (40)/1 = 40 (40 + 30)/2 = 35While the Table 2 dimension coordinates are such that disambiguation isnot appropriate for dimension coordinates other than a subset ofdimension coordinates characterized by a time to which the NOV-2005 timereporting label corresponds (i.e., associated with the disambiguationtime chamber defined by the NOV-2005 time period), for other dimensioncoordinates, it may be appropriate for there to be disambiguation fordimension coordinates of a subset of dimension coordinates associatedwith the disambiguation time chamber defined by the OCT-2005 time periodand/or for the dimension coordinates of a subset of dimensioncoordinates associated with the disambiguation time chamber defined bythe DEC-2005 time period (invoking determination 202 and/ordetermination 206).

As another alternative with respect to the Table 2 data, thedisambiguation for the dimension coordinates associated with thedisambiguation time chamber defined by the NOV-2005 time period couldresult in the TEAM value of Blue Team being attributed to Bill for theNOV-2005 time reporting label.

(Result 2-3) Month Red Team Blue Team OCTOBER 2005 (25 + 40)/2 = 32.5(30)/1 = 30 NOVEMBER 2005 (35)/1 = 35 (30 + (20 + 20))/2 = 35 DECEMBER2005 (40)/1 = 40 (40 + 30)/2 = 35It can thus be seen that, in general, a disambiguation may occurseparately for any or all disambiguation time chambers (which correspondto time reporting labels by being defined by time periods to which thetime reporting labels correspond) for which there is reporting based onthe report query.

Furthermore, unlike the FIG. 4 and FIG. 5 example, in which thedisambiguation time chambers each correspond to a separate singlerespective time reporting label, there may be examples in which thedisambiguation time chambers each correspond to more than one timereporting label. For example, the reporting labels may be at a monthresolution (e.g., JAN-2005, FEB-2005, . . . , NOV-2005 and DEC-2005) ofthe time dimension. The disambiguation time chambers, may on the otherhand, be at a quarter resolution (e.g., Q1 2005, Q2 2005, Q3 2005 and Q42005). In other words, all the dimension coordinates characterized by atime period that corresponds to any time reporting label for a month ina particular quarter would be associated with that particular quarterfor disambiguation purposes.

FIG. 6 illustrates such an example. Referring to FIG. 6, dimensioncoordinates are shown that are associated with time periodscorresponding to the time reporting labels JAN-2005 (302), FEB-2005(304), MAR-2005 (306), APR-2005 (312), MAY-2005 (314) and JUN-2005(316). Dimension coordinates associated with time periods correspondingto the remaining time reporting labels for 2005 are not shown, tosimplify the illustration.

Using the example of the month resolution and quarter resolution of the2005 time period, the disambiguation time chambers would be defined byeach quarter-year time period of the whole year, for a total of fourdisambiguation time chambers. Again, there are four disambiguation timechambers for the 2005 time period, even though there are twelve timereporting labels for the 2005 time period. That is, the disambiguationdecision for each PERSON grain entity could thus be made up to fourtimes, once for each quarter (e.g., determinations 308 and 318 for thefirst and second quarter, respectively), even though the reportprocessing is carried out twelve times, once for each “month” timereporting label.

In the discussion thus far, we have not described what criterion may beused to make particular disambiguation determinations (such as, forexample, the determination 412 in FIG. 4; the determinations 502, 504and 506 in FIG. 5; and the determinations 608 and 618 in FIG. 6).

In one example, relative to a disambiguation time chamber thatcorresponds to November 2005, it is supposed that Alice worked for RedTeam from before November 2005 and up until 6-NOV-2005. Alice worked forGreen Team from 7-NOV-2005 to 21-NOV-2005. Finally, Alice worked forBlue Team from 22-NOV-2005 until well after November 2005. Furthermore,Alice eats one cookie every day she works, and she works every day inNovember.

Further, suppose each team was allotted a bonus budget. Red Team's bonusbudget is $3K, Green Team's bonus budget is $5k and Blue Team's bonusbudget is $7k. Disambiguating Alice's team for the November 2005disambiguation time chamber, various criteria may be considered, withvarying results as to the coarser grain value (value at TEAM grain) toassociate with the finer grain value of Alice (value at PERSON grain).

For example, if criterion equals “latest team,” then the result is BlueTeam. If the criterion equals “earliest team,” then the result equalsRed Team. It is noted that the “latest team” and “earliest team”criteria are time based. Other criteria may include, for example,“longest team membership during time of disambiguation time chamber.”For this criterion, the results equals “Green Team.” For the criterionof “highest bonus budget,” the result equals Blue Team. For thecriterion of “team on which she ate the most cookies,” the result equals“Green Team.”

It can be seen, then, that many different criteria may be used.

We now discuss some particulars of the processing that may be done inview of the association of coarser grain values with finer grainervalues, where such an association may be a result of a disambiguation.In particular, processing for generating the report may includeassociating metric values in a “copying down” or “rolling up” direction.“Rolling up” includes associating, with a coarser grain value, a metricvalue that corresponds to a finer grain value (i.e., rolling up fromfiner to coarser) with which that coarser grain value is associated.Determining, for each month, the average number of cookies per person(metric value that corresponds to a finer grain value—at PERSON grain)eaten by each of Red Team and Blue Team (coarser grain value—at TEAMgrain) is an example of rolling up. In other words, “rolling up”includes associating in a many (finer grain values) to one (coarsergrain value) manner.

“Copying down” includes associating, with finer grain values, a metricvalue that corresponds to a coarser grain value (i.e., copying down fromcoarser to finer). An example of copying down includes, for each month,associating the team goal (coarser grain value—at TEAM grain) for everyperson (finer grain value—at PERSON grain). In other words, “copyingdown” includes associating in a one (coarser grain value) to many (finergrain value) manner.

In either case (copying down or rolling up), the disambiguation isuseful for resolving what coarser grain value is to be associated withfiner grain values, for which the association may otherwise beambiguous.

FIG. 7 is a block diagram illustrating an example architecture of asystem 700 in which reporting of facts of a dimensionally-modeled factcollection may be performed, including disambiguating as desired or asotherwise determined to be appropriate. Referring to FIG. 7, a user 702may cause a report query 704 to be provided to a fact collection querygenerator 706. For example, the user 702 may interact with a web pagevia a web browser, where the web page is served by a report userinterface using, for example, a Java Server Page mechanism. In thisexample, the user 702 interacts with the web page such that the reportquery 704 is provided to the fact collection query generator 706. Thereport query 404 includes a dimension coordinate constraint, which maybe one or more dimension coordinate constraints.

In general, a dimension coordinate constraint for a dimension of thedimensionally-modeled fact collection specifies coordinates of thatdimension of the dimensionally-modeled fact collection. For example, adimension coordinate constraint may specify coordinates of thatdimension of the dimensionally-modeled fact collection by specifying avalue of the dimension at a particular grain. Dimension coordinateconstraints of the report query 704, then, specify a plurality ofcoordinates of one or more dimensions of the dimensionally-modeled factcollection, on which a report is to be based. It is noted that the lackof an explicit constraint may imply a “null constraint” (which, in andof itself, may be considered a dimension coordinate constraint) forwhich the resulting plurality of dimension coordinates on which thereport is to be based, is all dimension coordinates from that dimension.

The fact collection query generator 706 processes the report query 704to generate an appropriate corresponding fact collection query 708,which is presented to the dimensionally-modeled fact collection 710. Aresult 716 of presenting the fact collection query 708 to thedimensionally-modeled fact collection 710 is processed by a reportgenerator 7418 to generate a report corresponding to the report query704 caused to be provided by the user 702. In particular, the generatedreport includes an indication of processing with respect to dimensionalmembers as appropriate in view of the dimension coordinate constraintsof the report query 704.

In one example, the dimensionally-modeled fact collection 710 isimplemented as a relational database, storing fact data in a manner thatis accessible to users according to a ROLAP—Relational Online AnalyticalProcessing—schema (fact and dimension tables). In this case, the factcollection query 708 may originate as a database query, in some formthat is processed into another form, for example, which is processed byan OLAP query engine into a fact collection query 708, presented as anSQL query that is understandable by the underlying relational database.This is just one example, however, and there are many other ways ofrepresenting and accessing a dimensionally-modeled fact collection.

Processing 718 is applied to the fact collection result 716 to generatea report. The generated report includes an indication of dimensionmembers and facts corresponding to those indicated dimension members.What facts are reported may depend, at least in part, on disambiguationof what coarser grain value is determined to correspond to particularfiner grain values.

Referring still to FIG. 7, the composition of the generated report maybe accomplished by the fact collection query generator 706 particularlygenerating the fact collection query 708 in accordance with the reportquery, by the result processing 718 particularly processing the factcollection result (e.g., by applying filtering) in accordance with thereport query, or by a combination of both.

As also illustrated in FIG. 7, the report query 704 may include adisambiguation criterion that may be provided, for example, via a userinterface. In some examples, in the absence of such a disambiguationcriterion, the manner in which the facts corresponding to thoseindicated dimension members are reported may be according to a defaultmode or according to a preconfigured mode. The fact collection querygenerator 706 and/or the result processing 718, as appropriate, operateaccording to the default, preconfigured or designated mode.

In accordance with one example, a multiple-pass processing is utilized.An example of the multiple-pass processing is illustrated by theflowchart of FIG. 8. In a first pass 802, every dimension coordinate(which may be one or more dimension coordinates) that satisfies thedimension coordinate constraint is determined. The determined dimensioncoordinates have a particular value at a particular grain. In anotherpass 804, it is determined which of the dimension coordinates are partof a subset of “ambiguous” dimension coordinates, meeting the followingconditions:

-   -   Each of the dimension coordinates of the subset is such that        there is at least one other dimension coordinate of the subset        having a value at the finer grain (“finer grain value”) that is        the same as the finer grain value of that dimension coordinate;        and    -   the at least one other dimension coordinate also has a value at        the coarser grain (“coarser grain value”) that is different from        the coarser grain value of that dimension coordinate.        That is, it is determined which of the dimension coordinates        satisfying the dimension coordinate constraint of the report        query are subject to the coarser grain value to finer grain        value association ambiguity.

At step 806, for every unique finer grain value of the dimensioncoordinates of the subset, it is determined what coarser grain value toassociate with all dimension coordinates of the subset having that finergrain value. In particular, the determined coarser grain value is thecoarser grain value of one of the dimension coordinates, of the subset,having that finer grain value. For each of the dimension coordinates ofthe plurality of dimension coordinates not in the subset, the coarsergrain value associated with that dimension coordinate is the coarsergrain value of that dimension coordinate. Steps 804 and 806 may berepeated for additional disambiguation time chambers. That is, thesubset would be determined for each additional disambiguation timechamber and disambiguation carried out as appropriate on that subset.Finally, at step 808, a report is generated in view of the plurality ofdimension coordinates and their associated coarser grain values.

We have thus described how a situation may be addressed in which asubset of a plurality of dimension coordinates satisfying a dimensioncoordinate constraint of a report query are such that, for eachdimension coordinate of the subset, there is at least one otherdimension coordinate of the subset having a finer grain value that isthe same as the finer grain value of that dimension coordinate and theat least one other dimension coordinate also has a coarser grain valuethat is different from the coarser grain value of that dimensioncoordinate. In particular, disambiguation may be carried out such that,for every unique finer grain value of the dimension coordinates of thesubset, the coarser grain value to associate with all dimensioncoordinates of the subset having that finer grain value is determined,wherein the determined coarser grain value is the coarser grain value ofone of the dimension coordinates, of the subset, having that finer grainvalue. A report may be generated in view of the plurality of dimensioncoordinates and their associated coarser grain values.

Unfettering Disclosure

The inventors have realized that it is desirable to consider thephenomenon in which, for each of at least one of the dimensioncoordinates that satisfy a dimension coordinate constraint of a reportquery, that dimension coordinate has a particular value at a grainassociated with the report query and there are other dimensioncoordinates that have that particular value at the associated grain andthat do not satisfy the dimension coordinate constraint. This phenomenonarises when one or more dimensions in which the dimension coordinatesexist is a slowly changing dimension. In addition, there are otherscenarios in which, for each of one or more of the dimension coordinatessatisfying the report query, that dimension coordinate has a particularvalue at a grain associated with the report query and there are otherdimension coordinates that have that particular value at the associatedgrain and that do not satisfy the dimension coordinate constraint.

We first discuss the well-known phenomenon in the field of dimensionaldata modeling of “slowly changing dimensions.” This is a phenomenon inwhich the relationship of grains for a dimension may change over time.While it may be contrived to consider the concept of slowly changingdimensions with reference to the example LOCATION dimension (since,generally, the relationship of CONTINENT, COUNTRY and CITY grains willnot change over time), there are other more realistic examples of thisphenomenon.

As one illustration, consider an EMPLOYEE dimension that is intended torepresent an organizational chart of a company. In this example, theEMPLOYEE dimension comprises the following grains: ORGANIZATION,DIVISION, TEAM and PERSON. Using this example, it can be seen thatvalues of coordinates at various grains may change as a person movesfrom one team to another team (or, perhaps, a team moves from onedivision to another division). For example, in one month, Joe worked onthe Red Team; the next month, he worked on the Blue Team. This may bemodeled by one EMPLOYEE dimension coordinate having the value “Joe” atgrain PERSON and the value “Red Team” at grain TEAM, plus a secondEMPLOYEE dimension coordinate also having the value “Joe” and grainPERSON but the value “Blue Team” at grain TEAM. It is also possible toencode in the representation of the dimension coordinates the specifictime intervals during which these grain relationships obtained.

As more background to the issue of temporal dimensions (slowly changingdimensions), we use an example relative to the EMPLOYEE dimension,mentioned above, intended to represent an organizational chart of acompany. The EMPLOYEE dimension comprises the grains of ORGANIZATION,DIVISION, TEAM and PERSON. In this example, various people have movedonto and off of the Red Team. This phenomenon is modeled by EMPLOYEEdimension coordinates having value “Red Team” at grain TEAM, and havingvalues indicative of those people at grain PERSON; these dimensionmembers and that grain relationship are further qualified by the timesduring which that relationship obtained. These dimension members may berepresented as follows:

TABLE 3 date range for which this value at value at PERSON value atPERSON grain has the value of grain TEAM grain “Red Team” at the TEAMgrain Joe Red Team 4 Jan. 2004 to 1 Mar. 2006 Mary Red Team 3 Mar. 2003to 18 Jul. 2006 Bill Red Team 1 Dec. 2005 to 12 Dec. 2005

Continuing with the example, and noticing that Bill is only on the RedTeam for part of December 2005, consider that Bill was on the Blue Teamfrom 13 Dec. 2005 to 31 Dec. 2005. Finally, also consider that thenumber of calls taken during December 2005 by Joe, Mary and Bill (andZoe as well) is represented as follows:

TABLE 4 date range (during value at December) for PERSON “number ofcalls” number of grain value at TEAM grain value calls taken Joe RedTeam 1 Dec. 2005 to 10 31 Dec. 2005 Mary Red Team 1 Dec. 2005 to 22 31Dec. 2005 Bill Red Team 1 Dec. 2005 to 8 12 Dec. 2005 Bill Blue Team 13Dec. 2005 to 6 31 Dec. 2005 Zoe Blue Team 1 Dec. 2005 to 12 31 Dec. 2005

Given the preceding information, a report query may request the numberof calls taken during December 2005 by persons who were on the Red Teamsometime during December 2005. There are some analytic scenarios inwhich a user may desire to know the number of calls taken by each personon the Red Team (including Bill) during December 2005 to include onlythose calls taken while that person was actually on the Red Team (adimension coordinate constraint). These may be thought of as reportingon facts in a “fettered” manner, since the facts accounted for in thereported results not only correspond to the dimension coordinateconstraint of persons on the Red Team but, in addition, are bound by thedimension coordinate constraint of persons on the Red Team (i.e.,include facts for persons on the Red Team only while those persons areactually on the Red Team). Thus, for example, the record indicating thesix calls taken by Bill while Bill was on the Blue team is not utilizedfor the report.

This is what would be reported conventionally. For example, using thepreceding information regarding calls taken, such a report (i.e., withBill being attributed only those calls taken during December 2005 byBill while Bill is actually on the Red Team) may be as follows:

TABLE 5 value at PERSON grain December 2005 Joe 10 Mary 22 Bill 8

For example, using the preceding information regarding calls taken byJoe, Mary and Bill, it may be useful for a user to know the number ofcalls taken by each person on the Red Team during December 2005, withoutregard for whether that person was actually on the Red Team when thecalls were taken. That is, it may be useful for the facts accounted forin the reported results, while corresponding in some sense to thedimension coordinate constraint of persons on the Red Team, to be notbound by the dimension coordinate constraint of persons on the Red Team(i.e., may include facts for persons on the Red Team even while thosepersons were not actually on the Red Team). Thus, for example, therecord indicating the six calls taken by Bill while Bill was on the Blueteam is utilized for this unfettered report.

Reporting on the facts in such an unfettered manner, Bill is attributedcalls taken during December 2005 without regard for the team of whichBill is a member when a particular call is taken, and the report wouldbe as follows:

TABLE 6 value at PERSON grain December 2005 Joe 10 Mary 22 Bill 14

Thus far, we have discussed scenarios arising due to the phenomenon ofslowly changing dimensions. As mentioned above, the inventors haverealized that there are other analytic scenarios in which it would beuseful for the facts to be reported in an “unfettered manner” (i.e., ina manner that corresponds to, but is not strictly bound by the dimensioncoordinate constraint of the report query). In general, such analyticscenarios arise in situations where a grain value of dimensioncoordinates satisfying the dimension coordinate constraint of a reportquery may also be the value, at that grain, for other dimensioncoordinates that do not satisfy the dimension coordinate constraint ofthe report query.

For example, referring to Table 7 below, a dimension coordinateconstraint of a report query may be with respect to weeks in the monthof January (ignoring the beginning of January for simplicity ofexplanation).

TABLE 7 January week 3 10 January week 4 22 January week 5 8 Februaryweek 5 6 February week 6 3Thus, with reference to Table 7, a report query for calls taken during aweek in January (the dimension coordinate constraint being, in informalterms, “a week in January”) would result in the dimension coordinatescharacterized by:

-   -   a. “January” at the month grain and “week 3” at the week grain;    -   b. “January” at the month grain and “week 4” at the week grain;        and    -   c. “January” at the month grain and “week 5” at the week grain.        However, the value of “week 5” at the week grain is also the        value, at the week grain, of the dimension coordinate        characterized by:    -   d. “February” at the month grain and “week 5” at the week grain.

Therefore, when reporting in a fettered manner, the reported “number ofcalls” in response to the report query of “calls taken during a week inJanuary” would result in a report of 10+22+8=40 calls. On the otherhand, when reporting in an unfettered manner, the reported “number ofcalls” in response to the same report query would result in a report of10+22+8+6=46 calls. That is, when reporting in a fettered manner, thesix calls occurring during week 5 in February would not be reported,since the portion of week 5 that is in February is not in January. Onthe other hand, when reporting in an unfettered manner, the six callsoccurring during week 5 in February would be reported. That is, thesecalls are still for a dimension coordinate with “week 5” at the weekgrain, even though the dimension coordinate does not satisfy the reportquery, since the dimension coordinate has the value “February” at themonth grain.

FIG. 9 is a block diagram illustrating an example architecture of asystem 900 in which reporting of facts of a dimensionally-modeled factcollection is performed in an unfettered manner. Referring to FIG. 9, auser 902 may cause a report query 904 to be provided to a factcollection query generator 906. For example, the user 902 may interactwith a web page via a web browser, where the web page is served by areport user interface using, for example, a Java Server Page mechanism.In this example, the user 902 interacts with the web page such that thereport query 904 is provided to the fact collection query generator 906.The report query 904 includes a dimension coordinate constraint, whichmay be one or more dimension coordinate constraints.

In general, a dimension coordinate constraint for a dimension of thedimensionally-modeled fact collection specifies coordinates of thatdimension of the dimensionally-modeled fact collection. For example, adimension coordinate constraint may specify coordinates of thatdimension of the dimensionally-modeled fact collection by specifying avalue of the dimension at a particular grain. Dimension coordinateconstraints of the report query 904, then, specify a subset ofcoordinates of one or more dimensions of the dimensionally-modeled factcollection, on which it is desired to report.

The fact collection query generator 906 processes the report query 904to generate an appropriate corresponding fact collection query 908,which is presented to the dimensionally-modeled fact collection 910. Aresult 916 of presenting the fact collection query 908 to thedimensionally-modeled fact collection 910 is processed by a reportgenerator 918 to generate a report corresponding to the report query 904caused to be provided by the user 902. In particular, the generatedreport includes an indication of dimensional members as appropriate inview of the dimensional coordinate constraints of the report query 904.

In one example, the dimensionally-modeled fact collection 910 isimplemented as a relational database, storing fact data in a manner thatis accessible to users according to a ROLAP—Relational Online AnalyticalProcessing—schema (fact and dimension tables). In this case, the factcollection query 908 may originate as a database query, in some formthat is processed into another form, for example, which is processed byan OLAP query engine into a fact collection query 908, presented as anSQL query that is understandable by the underlying relational database.This is just one example, however, and there are many other ways ofrepresenting and accessing a dimensionally-modeled fact collection.

Processing 918 is applied to the fact collection result 916 to generatea report. The generated report includes an indication of dimensionmembers and facts corresponding to those indicated dimension members.For example, the facts corresponding to those indicated dimensionmembers may be reported in an “unfettered” (i.e., in a manner that isnot bound by the dimension coordinate constraint of the report query)such as is discussed above relative to the example of Bill and the RedTeam. The facts corresponding to those indicated dimension members maybe reported in a “fettered” manner (i.e., in a manner that is bound bythe dimension coordinate constraints of the report query 904).

Referring still to FIG. 9, the composition of the generated report maybe accomplished by the fact collection query generator 906 particularlygenerating the fact collection query 908 in accordance with the reportquery, by the result processing 918 particularly processing the factcollection result (e.g., by applying filtering) in accordance withreport query, or by a combination of both.

As also illustrated in FIG. 9, the report query 904 may include anunfettered/fettered mode designation, which may be provided, forexample, via a user interface. In some examples, in the absence of suchan unfettered/fettered mode designation, the manner in which the factscorresponding to those indicated dimension members are reported may beaccording to a default mode or according to a preconfigured mode. Thefact collection query generator 106 and/or the result processing 918, asappropriate, operate according to the default, preconfigured ordesignated mode.

In accordance with one example, a multiple-pass processing is utilized.An example of the multiple-pass processing is illustrated by theflowchart of FIG. 10. In a first pass 1002, every dimension coordinate(which may be one or more dimension coordinates) that satisfies thedimension coordinate constraint is determined. The determined dimensioncoordinates have a particular value at a particular grain. In anotherpass 1004, at least one other dimension coordinate (which may be one ormore other dimension coordinates) is determined that does not satisfythe dimension coordinates constraint, wherein each of the at least oneother dimension coordinates has a value at the associated grain that isthe same as the value at the associated grain of one of the dimensioncoordinates determined in the first pass 1002. In yet another pass 1006,the facts of the dimensionally-modeled fact collection are determinedthat are specified by the determined dimension coordinates and by thedetermined at least one other dimension coordinate.

FIG. 11A illustrates steps of a specific example of generating a reportin an unfettered manner. Corresponding FIG. 11B provides supplementalexplanatory material for the FIG. 11A example. Referring to FIG. 11A, atstep 1102, a report query is received, and the report query includes adimension coordinate constraint of “persons on the Blue Team.” Oval 1152of FIG. 11B illustrates a formalistic representation of the dimensioncoordinate constraint and the associated grain. At step 1104 of FIG.11A, the received report query is processed to obtain all (one or more)dimension coordinates in which the value at the team grain is “BlueTeam.” Oval 1154 of FIG. 11B indicates that, in the example, theobtained dimension coordinates have a value of “Joe,” “Sally” or “Mista”at the Person grain (in addition to having the value of “Blue Team” atthe Team grain).

At step 1106, all (one or more) dimension coordinates are obtained thatsatisfy a replacement dimension coordinate constraint, including thegrain values obtained at step 1104 (“Joe,” “Sally” or “Mista”), withoutthe constraint of “person on the Blue Team.” Oval 1156 illustrates aformalistic representation of the step 1106 dimension coordinateconstraint.

At step 1108, operations corresponding to the report query are performedon the facts specified by the dimension coordinates obtained at step1106. That is, as illustrated by oval 1158, operations to generate thereport are performed on the facts specified by all dimension coordinatesfor which the value at the Person grain is “Joe,” “Sally” or “Mista” butfor which the value at the Team grain may be, but is not necessarily,“Blue Team.”

We have described herein a method/system which considers the phenomenonin which a particular value at a grain associated with a dimensioncoordinate constraint of a report query may also be the value, at theassociated grain, for other dimension coordinates that do not satisfythe dimension coordinate constraint of the report query.

Temporal Mode Specification Disclosure

As discussed in the Background, facts of a dimensionally-modeled factcollection correspond to locations in a dimensional data space accordingto which the dimensionally-modeled fact collection is modeled. Moreover,the relationship of grains of a dimension may change over time—aphenomenon known as “slowly changing dimensions” (and also referred toherein as “temporal dimensions”). As also mentioned in the Background,the inventors have realized that it is desirable to consider thephenomenon of slowly changing dimensions in the process of generating areport for a dimensionally-modeled fact collection.

We thus describe herein various methods that consider the phenomenon oftemporal dimensions in the process of reporting on facts of adimensionally-modeled fact collection. It is noted that, whileconsideration of the facts (or the presence or absence thereof) relativeto various coordinates is not precluded in the described methods, thefocus of the description is with respect to the coordinates themselves,including the grain relationships. If not evident already, thisdistinction should become evident upon further reading of this patentapplication.

Before describing the methods, however, we give more background to theissue of temporal dimensions (slowly changing dimensions), using anexample relative to the EMPLOYEE dimension, mentioned above, intended torepresent an organizational chart of a company. The EMPLOYEE dimensioncomprises the grains of ORGANIZATION, DIVISION, TEAM and PERSON. In thisexample, various people have moved onto and off of the Red Team. Thisphenomenon is modeled by EMPLOYEE dimension coordinates having value“Red Team” at grain TEAM, and having values indicative of those peopleat grain PERSON; these dimension members and that grain relationship arefurther qualified by the times during which that relationship obtained.These dimension members may be represented as follows:

date range for which this value at value at TEAM value at PERSON PERSONgrain has the value of grain grain “Red Team” at the TEAM grain Red TeamJoe January 2005-March 2005 Red Team Mary March 2005-July 2005 Red TeamBill June 2005-January 2006Continuing with the example, consider that the current date is January2006, and a user requests a report, for each month of the first quarterof 2005, for each person on the Red Team, that is, with respect tolocations specified by coordinates in the EMPLOYEE dimension at thePERSON grain, whose value at the TEAM grain is “Red Team.” Theparticular facts that correspond to specified locations, or even whetherthere exist such facts corresponding to particular locations, is notrelevant for the purpose of the example.

It is assumed that the report is to have two axes: an X axis for anindication of each month of the first quarter of 2005, and a Y axis foran indication of each person on the Red Team. However, given that theEMPLOYEE dimension is a temporal dimension, it is ambiguous as to whatvalues at the PERSON grain should be indicated on the Y axis of thereport. That is, while the TIME dimension coordinates of the report areconstrained to have value “Q1-2005” at the QUARTER grain, it isambiguous for what temporal extent of the EMPLOYEE dimension the user isrequesting the report. More specifically, it is ambiguous which of thevalues at the PERSON grain of the EMPLOYEE dimension should be indicatedon the Y axis of the report, given that the EMPLOYEE dimensioncoordinates of the report are constrained to have value “Red Team” atthe TEAM grain.

For example, Bill was not on the Red Team at all during the firstquarter of 2005. Perhaps Bill should not be included on the report sinceBill was not on the Red Team during the first quarter of 2005. Morespecifically, referring to the TIME dimension coordinate constraints ofthe report being “first quarter of 2005,” there is no member of theEMPLOYEE dimension having value “Bill” at grain PERSON and value “RedTeam” at grain TEAM within the temporal extent corresponding to “firstquarter of 2005.”

But, on the other hand, perhaps Bill should indeed be included on thereport, since Bill is currently on the Red Team (remember, it is assumedthat the current date is January 2006). More specifically, within thetemporal extent corresponding to “January 2006,” there is indeed amember of the EMPLOYEE dimension having value “Bill” at grain PERSON andvalue “Red Team” at grain TEAM.

As another example, Mary was on the Red Team only during part (March2005) of the first quarter of 2005. Particularly for January 2005 andFebruary 2005, perhaps Mary should not be included on the report. Or,perhaps, for January 2005 and February 2005, Mary should be included,even if Mary was on another team for those months, since Mary was on theRed Team during at least part of the first quarter of 2005.

It can be seen, then, given that the grain relationships of the EMPLOYEEdimension can change over time, that there is ambiguity as to whichvalues at one grain of the EMPLOYEE dimension (PERSON) should beconsidered related to values at another grain (TEAM) of the EMPLOYEEdimension. More particularly, there is ambiguity as to the time extentto utilize in determining, given constraints expressed, at the TEAMgrain of the EMPLOYEE dimension, upon the EMPLOYEE dimension coordinatesof the report, which values at the PERSON grain of the EMPLOYEEdimension should be considered to satisfy those constraints, and shouldtherefore be represented on the report.

In accordance with an aspect, a report query specifies a temporal mode,in addition to specifying at least one dimension coordinate constraint.The specified temporal mode is processed to determine a time extentdescriptor. The processing of the specified temporal mode may be in viewof the dimension coordinate constraints and/or a context. A factcollection query is generated, and a result of providing the factcollection query to the dimensionally-modeled fact collection isprocessed.

The processed result includes an indication of dimensional values asappropriate in view of the time information from the time extentdescriptor. More particularly, the time extent descriptor includesinformation about a period of time (i.e., from a “starting time” to an“ending time”) to utilize in determining which values at one grain of adimension should be considered to be also present at another grain(e.g., a coarser grain) of that dimension.

FIG. 12 is a block diagram illustrating an example architecture of asystem 100 in which the processing of the temporal mode, to determine atime extent descriptor, as well as generating a fact collection query,may take place. Referring to FIG. 12, a user 1202 may cause a reportquery 1204 to be provided to a fact collection query generator 1206. Forexample, the user 1202 may interact with a web page via a web browser,where the web page is served by a report user interface using, forexample, a Java Server Page mechanism. In this example, the user 1202interacts with the web page such that the report query 1204 is providedto the fact collection query generator 1206. The report query 1204includes a temporal mode specification and at least one dimensioncoordinate constraint.

The temporal mode specification of the report query 1204 is a symbolicrepresentation of a function to determine a time extent descriptor. In asimple example, the temporal mode specification may be human-readabletext such as “Now,” “Beginning of Report” or “End of Report.” Thetemporal mode processor 1212 processes the temporal mode specification1205, provided as part of the report query 1204, to determine a timeextent descriptor. (Later, we discuss in greater detail how thesetemporal mode specifications, and others, may be processed according toa function to determine a time extent descriptor.)

We now discuss the dimension coordinate constraints of the report query1204. In general, a dimension coordinate constraint for a dimension ofthe dimensionally-modeled fact collection specifies coordinates of thatdimension of the dimensionally-modeled fact collection. For example, adimension coordinate constraint may specify coordinates of thatdimension of the dimensionally-modeled fact collection by specifying avalue of the dimension at a particular grain. Dimension coordinateconstrains of the report query 1204, then, specify a subset ofcoordinates of one or more dimensions of the dimensionally-modeled factcollection, on which it is desired to report.

The fact collection query generator 1206 processes the report query 1204to generate an appropriate corresponding fact collection query 1208,which is presented to the dimensionally-modeled fact collection 1210. Aresult 1216 of presenting the fact collection query 1208 to thedimensionally-modeled fact collection 1210 is processed by a reportgenerator 1218 to generate a report corresponding to the report query1204 caused to be provided by the user 1202. In particular, thegenerated report includes an indication of dimensional members asappropriate in view of the time information 1214 from the time extentdescriptor 1212, determined from the temporal mode 1205.

In one example, the dimensionally-modeled fact collection 1210 isimplemented as a relational database, storing fact data in a manner thatis accessible to users according to a ROLAP—Relational Online AnalyticalProcessing—schema (fact and dimension tables). In this case, the factcollection query 108 may originate as a database query, in some formwhich is processed into another form, for example, which is processed byan OLAP query engine into a fact collection query 1208, presented as anSQL query that is understandable by the underlying relational database.This is just one example, however, and there are many other ways ofrepresenting and accessing a dimensionally-modeled fact collection.

FIG. 12 illustrates two modes of operation of the system 100, indicatedin FIG. 12 as “Option A” and “Option B.” Basically, “Option A” refers toapplying the time information 114 from the time extent descriptor to theprocessing 106 to generate the fact collection query 1208. “Option B”refers to applying the time information 1214 from the time extentdescriptor to the processing 1218 to generate the report. In accordancewith either option, the generated report includes an indication ofdimensional members as appropriate in view of the time information 1214from the time extent descriptor 1212, determined from the temporal mode1205.

Having generally described a system and process according to which agenerated report includes appropriate indications of dimensionalmembers, we now provide some illustrations of the concept with examples.Furthermore, from the examples, it can be further seen how the focus ofthe described system and process is with respect to the coordinatesthemselves, including the grain relationships, and not withconsideration of the facts (or the presence or absence thereof) relativeto various coordinates.

In the following examples, illustrated in FIGS. 13 to 16, processing isonly in one dimension, the EMPLOYEE dimension. As discussed above, theEMPLOYEE dimension is a temporal dimension, since the relationship ofgrains of the EMPLOYEE dimension may change over time. Further, forexamples in which it is relevant, the context includes an indication ofthe current date being 16 Mar. 2005. Typically, the context is relevantfor those report queries in which the function by which the temporalmode is processed to determine a time extent descriptor has, as arequired parameter, some indication of the environment in which thereport query is made. While the function according to which the temporalmode may be processed, to determine the time extent descriptor, may bearbitrarily defined, in the examples, we apply functions that correspondto the ordinarily understood meaning of the example specified temporalmodes.

The report queries in the examples of FIGS. 13 to 16 all request valuesof coordinates at the PERSON grain of the EMPLOYEE dimension byconstraining those coordinates to have a value of “Red Team” at the TEAMgrain. That is, the dimension coordinate constraint broadly indicates asubset of the dimensionally modeled fact collection specified bycoordinates at least having a value of “Red Team” at the TEAM grain ofthe EMPLOYEE dimension. As can be seen from the examples, withoutknowing the time information from a time extent descriptor determined byprocessing the specified temporal mode, it is ambiguous which of thosecoordinates having a value of “Red Team” at the TEAM grain of theEMPLOYEE dimension are actually being requested.

FIG. 13 illustrates an example of processing a report query in which thespecified temporal mode is “all time.” In some sense, this may beconsidered the same as not providing a temporal mode at all. Withrespect to the time information (1214, in FIG. 12) from the time extentdescriptor resulting from processing (1212, in FIG. 12) the specifiedtemporal mode (1205, in FIG. 12), the “starting time” is theoreticallythe beginning of time (BOT) and the ending time is theoretically the endof time (EOT). As a practical matter, the “starting time” may be set toa time value that is at least as early as the earliest time for whichthere exists a coordinate of the EMPLOYEE dimension that has the value“Red Team” at the TEAM grain, and the “ending time” may be set to a timevalue that is at least as late as the latest time for which there existsa coordinate of the EMPLOYEE dimension that has the value “Red Team” atthe TEAM grain.

The resulting report should thus include indications of dimensioncoordinates (e.g., labels) for all values, at the PERSON grain, ofcoordinates having the value of “Red Team” at the TEAM grain, at anytime. Since FIG. 13 includes all the people who are members of the RedTeam at any time during “all time,” the resulting report should includeindications of dimension coordinates for Joe, Jean, Alfonse, Esmerelda,Joaquin and Eva.

It should be noted that the report query may not even specify facts tobe reported. As noted above, the focus of the description is withrespect to the coordinates themselves, including the grainrelationships. If, for example, the report query did not specify factsto be reported, the resulting report may contain labels only, but nofacts. This can be useful, for example, to report on the grainrelationships themselves (in consideration of their temporal nature),without consideration for the underlying facts at the locationsspecified by the coordinates existing at those grains.

Now, turning to FIG. 14, this figure illustrates an example ofprocessing a report query in which the temporal mode is specified withrespect to the time coordinates of the report. In the example, the timecoordinates of the report are 2 Feb. 2005 (“starting time”) to 16 Mar.2005 (“ending time”). It can be seen, then, that during the time perioddefined by the “starting time” and the “ending time,” neither Joaquinnor Joe are members of the Red Team. In one example, themultidimensional fact collection includes metadata that providesinformation from which the temporal characteristics of the grainrelationships can be discerned. (See, for example, the article entitledKimball Design Tip #8: Perfectly Partitioning History With The Type 2Slowly Changing Dimension,” available athttp://www.kimballgroup.com/html/designtipsPDF/DesignTips2000%20/KimballDT8Perfectly.pdf,which describes augmenting dimension records with “time stamps” totemporally characterize the dimension records.) The resulting reportshould thus include indications of dimension coordinates for Jean,Alfonse, Esmerelda, and Eva, but should not include indications ofdimension coordinates for Joaquin and Joe.

FIG. 15 illustrates an example in which the temporal mode is specifiedas “now,” while the report coordinates are the same as in the FIG. 14example. Unlike the FIG. 14 example, the context is used in determiningthe time information from the extent descriptor from the temporal mode.It can be seen that, during the time period defined by the starting timeof “16 Mar. 2005” and the ending time of “16 Mar. 2005” (rememberingthat the current time is “16 Mar. 2005”), only Alfonse and Esmerelda aremembers of the Red Team. Joe, Jean, Joaquin and Eva are not members ofthe Red Team during this time period. The resulting report should thusinclude indications of dimension coordinates for Alfonse and Esmerelda,but should not include indications of dimension coordinates for Joe,Jean, Joaquin and Eva. The time extent descriptor in this example isindependent of the report coordinates.

In a final example, illustrated in FIG. 16, the temporal mode isspecified as a discontinuous span of three weeks. While the situation issomewhat contrived, it is viable nonetheless. The specification of thetemporal mode depends neither on the report coordinates nor on thecontext. The time information from the extent descriptor includes twoseparate pairs of starting and ending times. The first pair includes a“starting time” of 2 Feb. 2005 and an “ending time” of 15 Feb. 2005. Thesecond pair includes a “starting time” of 16 Mar. 2005 and an “endingtime” of 23 Mar. 2005. During these time periods, Joe, Jean, Alfonse,and Esmerelda are members of the Red Team. During these time periods,Joaquin and Eva are not members of the Red Team.

It should be realized, then, from the previous examples, that thespecified temporal mode may be processed in view of the context in whichthe report query is made, but for other queries, the specified temporalmode may be processed completely independent of such a context. Theexamples also illustrate that the specified temporal mode may beprocessed in view of the TIME coordinates of the report, but for otherqueries, the specified temporal mode may be processed completelyindependent of the TIME coordinates of the report. Furthermore,regardless of the manner in which the specified temporal mode may beprocessed, the resulting indications of dimension coordinates on thereport are not dependent on the facts (or the presence or absencethereof) associated with the various coordinates indicated on thereport.

In some examples, the specification of the temporal mode is via a userinterface. FIG. 17 illustrates an example of a report display screen1702, including functionality for a report configuration menu 1704 tospecify, among other things, the temporal mode of the report query. Inparticular, the “labels as of:” portion 1706 of the configuration menu1704 provides a listing of various possible temporal modes. Some ofthese temporal modes have been discussed with reference to FIGS. 13 to16. A user may interact with the configuration menu 1704 to choose oneof the listed temporal modes in the portion 1706, and the specifiedtemporal mode is provided as part of the report query (see, e.g., thereport query 1204 in FIG. 12). As discussed above, the temporal modespecification is a symbolic representation of a function to determine atime extent descriptor. The portion 1706 of the configuration menu 1704,then, provides a user-friendly way to choose the desired function.

We have described herein a method/system that considers the phenomenonof slowly changing dimensions in the process of reporting on facts of acollection of facts organized as, or otherwise accessible according to,a dimensionally-modeled fact collection. More specifically, we havedescribed a method/system whereby a report query may specify a temporalmode, and the specified temporal mode may be processed to determine atime extent descriptor. A processed result of querying thedimensionally-modeled fact collection includes appropriate dimensionallabels in view of the time information from the time extent descriptor.

Furthermore, we have more generally described a method in whichprocessing an evaluative query to determine performance facts and/orperformance parameters, on which determination of the evaluative scoreis based, includes processing the temporal relationships of finer grainvalues to coarser grain values for the dimension coordinates. Byconsidering the temporal relationships of finer grain values to coarsergrain values for dimension coordinates in the dimensionally-modeled factcollection, the evaluative score may be determined in a manner thatprovides a more accurate evaluation, in light of historical occurrencesrepresented by the dimensionally-modeled fact collection.

We now provide an example that illustrates and ties together someconcepts described above. The example considers a hypothetical team,TeamA, in the month of March, 2007. Agents Thru and Vayk were assignedto TeamA for the entire month, although Vayk was on vacation for thewhole month. Agent Erly began the month assigned to TeamA, buttransferred to TeamC mid-month. Agent Late began the month assigned toTeamB, but transferred onto TeamA midmonth. Agent Irrl once in thedistant past was assigned to TeamA, but was assigned to TeamC for theentirety of March.

These entries are in the temporal Organization dimension, then:

==Members of the Organization dimension, values at Agent:Team grains:

-   -   Thru:TeamA    -   Late:TeamA    -   Erly:TeamA    -   Irrl:TeamA    -   Vayk:TeamA    -   Late:TeamB    -   Irrl:TeamC    -   Erly:TeamC

Now, looking at performance data recorded for these dimension members,aggregated up to the month level. The raw facts for March-2007 are::

Sales Calls QScore QPoss Thru: TeamA 10,000  1,600 450 500 Late: TeamA3,000 500 200 300 Erly: TeamA 5,000 800 250 300 Irrl: TeamA — — — —Vayk: TeamA — — — — Late: TeamB 3,000 500 200 300 Irrl: TeamC 8,000 600150 300 Erly: TeamC 5,000 800 150 200

A straightforward OLAP report about TeamA data during the month ofMarch-2007, aggregated only up to the level of each agent, might looklike the following.

== Straightforward OLAP: Sales Calls QScore QPoss Thru: TeamA 10,0001,600 450 500 Late: TeamA 3,000 500 200 300 Erly: TeamA 5,000 800 250300 Irrl: TeamA — — — — Vayk: TeamA — — — —

It may be noticed that all coordinates possess the value TeamA at grainTeam, which is how they satisfy the coordinate constraint defining thisreport. Thus, agent Irrl, who was never assigned to TeamA during March(the time of this report) nonetheless is included as a member (i.e.,there is a label for Irrl on the report). By specifying the temporalextent as “the time of the report”, this can be improved on a little.This technique effectively removes agent Irrl from the report.

== Temporal extent = report duration: Sales Calls QScore QPoss Thru:TeamA 10,000 1,600 450 500 Late: TeamA 3,000 500 200 300 Erly: TeamA5,000 800 250 300 Vayk: TeamA — — — —

Alternately, the temporal extent could be specified as “the end of thereport” to just show those agents still assigned to TeamA at the end ofMarch:

== Temporal extent = report end: Sales Calls QScore QPoss Thru: TeamA10,000 1,600 450 500 Late: TeamA 3,000 500 200 300 Vayk: TeamA — — — —

But, in all these cases, it is noticed that the various agents have verydifferent values for their performance facts. This is because some ofthe facts are excluded for these agents—specifically, facts reflectingperformances while those agents were assigned to other teams. However,by “unfettering” the data query for the agents once the set of agents isdetermined, from the Team-level constraint originally used to define thereport, this allows all data for these agents for the month of March tobe included. Keeping the temporal extent as “end of report”, butunfettering the query, what is then obtained is:

== Unfettered Sales Calls QScore QPoss Thru: * 10,000 1,600 450 500Late: * 6,000 1,000 400 600 Vayk: * — — — —Now it can be seen that agent Late's number has gone up (i.e., includeperformance early in the month while not on TeamA) while agent Thru'snumbers have stayed the same.

In some examples, it is desirable to use the performance numbers toestablish a basis of comparison for agent performance. For example, the“average performance” facts for this Team may be computed—defined, inthis example, as the mean of the performance metric values using astatistical population comprising a Team's Agent-level members, thoughother statistical measures may be employed. Using the Agent-level,unfettered facts from “Unfettered” above, what is obtained is:

== Unfettered AgtAvg: (not considering agents such as Vayk for whomthere was no data) AgtAvg_(—) AgtAvg_(—) AgtAvg_(—) AgtAvg_(—) SalesCalls QScore QPoss TeamA 8,000 1,300 425 550

This is contrasted with values that would be computed without theunfettered data—that is, using just data associated with coordinateshaving a value of TeamA at the Team grain.

== Vanilla OLAP AgtAvg AgtAvg_(—) AgtAvg_(—) AgtAvg_(—) AgtAvg_(—) SalesCalls QScore QPoss TeamA 6,000 967 300 367

Clearly the unfettered reporting and average give (a) a betterreflection, in least in some sense, of individual agent performance, and(b) a better measure against which to compare the agent's performancethan do the vanilla OLAP results.

We now consider a slightly different presentation of this sameunderlying idea. It is desired to see the performance of many individualagents—say, all the agents at a given Site, SiteOne—and be able tocompare each agent to a standard of performance. Specifically, it isdesired to use the same standard of performance as before: theperformance of an average individual working on the same Team.

An “average agent's” performance for TeamA has been computed. This isalso done for all known Teams: TeamA, TeamB, and TeamC.

To do this involves effectively querying for all the agents inSiteOne—i.e., using a dimension coordinate constraint expressed at theSite level. This is analogous to the earlier constraint expressed at theTeam level to select just agents on TeamA. (As such, it also comes withanalogous considerations of temporal extent and fettering if there istemporal motion of Agents among Sites; which is ignored for thisdiscussion.)

== Performance facts for Agents in SiteOne: Sales Calls QScore QPossThru: SiteOne 10,000 1,600 450 500 Late: SiteOne 6,000 1,000 400 600Erly: SiteOne 10,000 1,600 400 500 Irrl: SiteOne 8,000 600 150 300 Vayk:SiteOne — — — —

It may be desired to add to this report, for each agent and for eachtype of fact, the aforementioned average fact value—that is, the valuefor an average Agent in the Team. But which Agent numbers contribute towhich Team's average? And to which Team's average should each agent becompared?

== Teams' averages - What goes here?: AgtAvg_(—) AgtAvg_(—) AgtAvg_(—)AgtAvg_(—) Sales Calls QScore QPoss TeamA: SiteOne ??? ??? ??? ???TeamB: SiteOne ??? ??? ??? ??? TeamC: SiteOne ??? ??? ??? ???

Before, when membership in TeamA was a given (because of that coordinateconstraint), the options were just around inclusion or exclusion ofAgent-level members in the report (temporal extent), and inclusion orexclusion of extra data for those agents outside of the TeamA constraint(unfettering). But when taking the average for the members of thatsingle Team, it was clear to which average each Agent's performance factwas contributing: that single Team's.

Now that many teams are in the scope of the query, there is a bit of adilemma. One could do the straightforward OLAP processing, and evaluatea simple Team level aggregate along the lines of SUM(fact)/COUNT(fact).

Looking at those operands aggregated “naturally” to the Team grain:

== Team-level aggregate facts - the simple OLAP way: SUM(x) Sales CallsQScore QPoss COUNT(x) TeamA: SiteOne 18,000 2,900 900 1100 3 TeamB:SiteOne 3,000 500 200 300 1 TeamC: SiteOne 13,000 1,400 300 500 2

Then the “bad average” is defined as above, i.e.,

BadAvg_(—) x::=SUM(x)/COUNT(x)

And it is simple to compute:

== Teams' averages - the simple OLAP way: BadAvg_(—) BAdAvg_(—)BadAvg_(—) BadAvg_(—) Sales Calls QScore QPos COUNT(x) TeamA: 6,000 967300 367 3 SiteOne TeamB: 3,000 500 200 300 1 SiteOne TeamC: 6,500 700150 250 2 SiteOne

It may be noticed, then, that the TeamA row contains the same valuesobtained for TeamA using a “fettered” query before, in “Vanilla OLAPAgtAvg”.

These numbers all seem low compared to the “Unfettered AgtAvg” wecomputed for TeamA. The reason is that none of these averages reflectsthe activity of a WHOLE NUMBER of agents for an ENTIRE MONTH. Forexample, performance facts for Agent Erly are contributing partly to theaverage for TeamA, and partly to the average for TeamC.

There are ways around this. One could weight the average by a measure ofhow many days of participation the performance facts represented, forexample. With the methods described, this need not be done, however.Further, an aggregate (Team) can be credited with the ENTIRETY of one ofits members performances for a time period. And it can be determinedwhich aggregate is used when comparing to a given person.

Turning back to the example, it can be remembered that there are onlyfive Agents in the sample fact set, and only four of them generate anyfacts during March. Regrouping the dimension members:

==Members of the Organization dimension, at Agent:Team grains,regrouped:

-   -   Thru:TeamA    -   Vayk:TeamA [XXX contributes no data during March]    -   Late:TeamA    -   Late:TeamB    -   Erly:TeamA    -   Erly:TeamC    -   Irrl:TeamA [XXX long before March]    -   Irrl:TeamC

If one would like all of a given Agent's data for March to contribute tojust one Team's average, how is this done? For some agents, it is fairlyevident—e.g., TeamA is the only possible choice for Agent Thru. Forothers, it's ambiguous. For example, Agent Erly's data could be seen ascontributing to TeamA's average, or alternately to TeamC's average, ashe was assigned to both of them during March.

Determining a one-to-one mapping between the fine-grained values(Agents) and coarse-grained values is called disambiguation. It istypically performed using a time-based rule, like “choose the latestassociation”. (“Chambering” could also be discussed relative to thismapping to March.) Using this disambiguation rule, the following mappingis derived.

==Disambiguated Agent—>Team mapping for the Organization dimension

-   -   Thru—>TeamA    -   Vayk—>TeamA [XXX contributes no data during March]    -   Late—>TeamA    -   Erly—>TeamC    -   Irrl—>TeamC

This mapping is used to determine which Agents' data contribute to eachTeam's overall statistics. In contrast to the simple OLAP approach, theresult is:

== Agent level facts aggregated to disambiguated Team: SUM(x) SalesCalls QScore QPoss COUNT(x) TeamA: SiteOne 16,000 2,600 850 1100 2TeamB: SiteOne — — — — 0 TeamC: SiteOne 18,000 2,200 600  800 2

And the average is again simple to compute:

== Average agent within disambiguated Teams: AgtAvg_(—) AgtAvg_(—)AgtAvg_(—) AgtAvg_(—) Sales Calls QScore QPoss COUNT(x) TeamA: 8,0001,300 425 550 2 SiteOne TeamB: — — — — 0 SiteOne TeamC: 9,000 1,100 300400 2 SiteOne

The disambiguated mapping is used once more to determine which of theseaverages to use as a standard of performance for a given Agent. That is,the average for the Team to which all that Agent's data was contributedis used under the same disambiguation mapping. This allows completion ofthe Agent-level performance comparison for all the agents withinSiteOne.

As an aside, to make the two views more consistent (i.e., single-teamversus all agents with the site), it may be worth employing a “bestpractice”: have temporal extent rules follow the disambiguation rules.e.g., temporal extent is “latest association” and disambiguation rule is“end-of-report.” That is, notice that the TeamA's average is the same asthe one computed in “Unfettered AgtAvg” above. This is because thechoice of temporal extent=“end of report” for that computation, whichdelivers comparable semantics to choosing the disambiguation rule“latest association” in the broader “Average agent within disambiguatedTeams” analysis just completed. In the former case, all data for eachindividual was used (i.e., unfettered), but just for those individualsassociated with TeamA at the end of the period. In the latter case, alldata for each individual was used (because all the data is associatedwith some SiteOne individual), and when picking which average theycontributed to, the latest association was used (disambiguation). Thismakes the numbers match up between the views.

== Performance facts & benchmarks for Agents in SiteOne: SalesAgtAvg_Sales Calls AgtAvg_Calls QScore AgtAvg_QScore QPoss AgtAvg_QPossThru:SiteOne 10,000 8,000 1,600 1,300 450 425 500 550 Late:SiteOne 6,0008,000 1,000 1,300 400 425 600 550 Erly:SiteOne 10,000 9,000 1,600 1,100400 300 500 400 Irrl:SiteOne 8,000 9,000 600 1,100 150 300 300 400Vayk:SiteOne — 8,000 — 1,300 — 425 — 550

1. A method of processing a performance query to a dimensionally-modeledfact collection by processing dimension coordinates that exist within adimensional data model of an organization having individual businessagents assigned to organizational groups, wherein facts of thedimensionally-modeled fact collection are accessible according to thedimensional data model and wherein the dimension coordinates have afirst particular grain (“finer grain”) that is finer than a secondparticular grain (“coarser grain”), the method to determine anevaluative score for a particular value at the finer grain (“finer grainvalue”) based on performance facts for dimension coordinates associatedwith the particular finer grain value, the method comprising:determining at least one performance parameter, relative to a particularvalue at the coarser grain (“coarser grain value”), against which tomeasure the performance facts associated with the finer grain value,including processing temporal relationships of finer grain values tocoarser grain values for the dimension coordinates, wherein processingthe temporal relationships of finer grain values to coarser grain valuesincludes processing data indicative of how relationships of the finergrain values to coarser grain values change over time and, basedthereon, processing performance facts based on performance facts fordimension coordinates having the particular coarser grain value;determining the evaluative score for the particular finer grain valuebased on performance facts of dimension coordinates having theparticular finer grain value, in view of the at least one determinedperformance parameter; wherein processing the temporal relationships offiner grain values to coarser grain values includes performing at leastone of unfettering and disambiguation to account for a changingrelationship of grains so that the evaluative score is adjusted toprovide a different type of evaluation score than a score based on astatic relationship of grain values; said disambiguation comprisingremapping the correspondence of fine-grained entities to coarser-grainedentities when required to remove an ambiguity in how a fine grainedentity maps to a coarser grained entity; and said unfettering comprisingre-expressing the constraint in terms of finer-grained entities ofdimension coordinates satisfying an original, coarser-grained constraintwhen required to account for a time changing relationship between finergrains and coarser grains; wherein the relationships of agents to groupsis not static and a report is generated including at least one of agroup score and an agent score in which said processing of temporalrelationships affects scoring.
 2. The method of claim 1, wherein the atleast one of a group score and an agent score includes a salesperformance.
 3. The method of claim 1, wherein the at least one of agroup score and agent score includes a call performance.
 4. The methodof claim 1, wherein said disambiguation comprises: processing dataindicative of how the relationships of finer grain values to coarsergrain values changes over time includes, for the particular coarsergrain value, determining a set of finer grain values for dimensioncoordinates having the particular coarser grain value; determining a setof dimension coordinates having finer grain values in the determined setof finer grain values, including dimension coordinates having finergrain values in the determined set of finer grain values but not havingthe particular coarser grain value.
 5. The method of claim 1, whereinsaid unfettering comprises: for the dimension coordinates having theparticular finer grain value, on which the determination of evaluativescore for the particular finer grain value is based, at least some ofthose dimension coordinates do not have the particular coarser grainvalue.
 6. The method of claim 5, wherein: determining a set of dimensioncoordinates having finer grain values in the determined set of finergrain values, including dimension coordinates having finer grain valuesin the determined set of finer grain values but not having theparticular coarser grain value includes: processing a query constraintthat is expressed in terms of the particular coarser grain value todetermine an initial set of dimension coordinates; and rewriting andreapplying the query constraint in terms of finer grain values of thedetermined initial set of dimension coordinates, to determine the set ofdimension coordinates.
 7. The method of claim 1, wherein: the particularfiner grain value is a first particular finer grain value of a pluralityof particular finer grain values; and the method further comprises:processing a temporal mode to determine a time extent descriptor; anddetermining the plurality of finer grain values based at least in parton processing the determined time extent descriptor.
 8. A computerprogram product for processing a performance query to adimensionally-modeled fact collection by processing dimensioncoordinates that exist within a dimensional data model, wherein facts ofthe dimensionally-modeled fact collection of an organization havingindividual business agents assigned to organizational groups areaccessible according to the dimensional data model and wherein thedimension coordinates have a first particular grain (“finer grain”) thatis finer than a second particular grain (“coarser grain”), the method todetermine an evaluative score for a particular value at the finer grain(“finer grain value”) based on performance facts for dimensioncoordinates associated with the particular finer grain value, thecomputer program product comprising at least one computer-readablemedium having computer program instructions stored therein which areoperable to cause at least one computing device to: determine at leastone performance parameter, relative to a particular value at the coarsergrain (“coarser grain value”), against which to measure the performancefacts associated with the finer grain value, including processingtemporal relationships of finer grain values to coarser grain values forthe dimension coordinates, wherein processing the temporal relationshipsof finer grain values to coarser grain values includes processing dataindicative of how relationships of the finer grain values to coarsergrain values change over time and, based thereon, processing performancefacts based on performance facts for dimension coordinates having theparticular coarser grain value; and determine the evaluative score forthe particular finer grain value based on performance facts of dimensioncoordinates having the particular finer grain value, in view of the atleast one determined performance parameter; wherein processing thetemporal relationships of finer grain values to coarser grain valuesincludes performing at least one of unfettering and disambiguation toaccount for a changing relationship of grains so that the evaluativescore is adjusted to provide a different type of evaluation score than ascore based on a static relationship of grain values; saiddisambiguation comprising remapping the correspondence of fine-grainedentities to coarser-grained entities when required to remove anambiguity in how a fine grained entity maps to a coarser grained entity;and said unfettering comprising re-expressing the constraint in terms offiner-grained entities of dimension coordinates satisfying an original,coarser-grained constraint when required to account for a time changingrelationship between finer grains and coarser grains; wherein therelationships of agents to groups is not static and a report isgenerated including at least one of a group score and an agent score inwhich said processing of temporal relationships affects scoring.
 9. Thecomputer program product of claim 8, wherein the at least one of a groupscore and an agent score includes a sales performance.
 10. The computerprogram product of claim 8, wherein the at least one of a group scoreand an agent score includes a call performance.
 11. The computer programproduct of claim 8, wherein said disambiguation comprises: processingdata indicative of how the relationships of finer grain values tocoarser grain values changes over time includes, for the particularcoarser grain value, includes determining a set of finer grain valuesfor dimension coordinates having the particular coarser grain value;determining a set of dimension coordinates having finer grain values inthe determined set of finer grain values, including dimensioncoordinates having finer grain values in the determined set of finergrain values but not having the particular coarser grain value.
 12. Thecomputer program product of claim 8, wherein said unfettering comprises:for the dimension coordinates, having the particular finer grain value,on which the determination of performance score for the particular finergrain value is based, at least some of those dimension coordinates donot have the particular coarser grain value.
 13. The computer programproduct of claim 12, wherein: determining a set of dimension coordinateshaving finer grain values in the determined set of finer grain values,including dimension coordinates having finer grain values in thedetermined set of finer grain values but not having the particularcoarser grain value includes: processing a query constraint that isexpressed in terms of the particular coarser grain value to determine aninitial set of dimension coordinates; and rewriting and reapplying thequery constraint in terms of finer grain values of the determinedinitial set of dimension coordinates, to determine the set of dimensioncoordinates.
 14. The computer program product of claim 8, wherein: theparticular finer grain value is a first particular finer grain value ofa plurality of particular finer grain values; and the computer programinstructions are further operable to cause at least one computing deviceto: process a temporal mode to determine a time extent descriptor; anddetermine the plurality of finer grain values based at least in part onprocessing the determined time extent descriptor.
 15. A computer systemconfigured to process a performance query to a dimensionally-modeledfact collection by processing dimension coordinates that exist within adimensional data model, wherein facts of the dimensionally-modeled factcollection of an organization having individual business agents assignedto organizational groups are accessible according to the dimensionaldata model and wherein the dimension coordinates have a first particulargrain (“finer grain”) that is finer than a second particular grain(“coarser grain”), the method to determine an evaluative score for aparticular value at the finer grain (“finer grain value”) based onperformance facts for dimension coordinates associated with theparticular finer grain value, the computer system configured to:determine performance parameters, relative to a particular coarser grainvalue, against which to measure the performance facts associated withthe finer grain value, including processing the temporal relationshipsof finer grain values to coarser grain values for the dimensioncoordinates; and determine the evaluative score for the particular finergrain value based on performance facts of dimension coordinates havingthe particular finer grain value, in view of the determined performanceparameters; wherein processing the temporal relationships of finer grainvalues to coarser grain values includes performing at least one ofunfettering and disambiguation to account for a changing relationship ofgrains so that the evaluative score is adjusted to provide a differenttype of evaluation score than a score based on a static relationship ofgrain values; said disambiguation comprising remapping thecorrespondence of fine-grained entities to coarser-grained entities whenrequired to remove an ambiguity in how a fine grained entity maps to acoarser grained entity; and said unfettering comprising re-expressingthe constraint in terms of finer-grained entities of dimensioncoordinates satisfying an original, coarser-grained constraint whenrequired to account for a time changing relationship between finergrains and coarser grains; wherein the relationships of agents to groupis not static and a report is generated including at least one of agroup score and an agent score in which said processing of temporalrelationships affects scoring. wherein the relationships of agents togroups is not static and a report is generated including at least one ofa group score and an agent score in which said processing of temporalrelationships affects scoring.
 16. The computer system of claim 15,wherein the at least one of a group score and an agent score includes asales performance.
 17. The computer system of claim 15, wherein the atleast one of a group score and an agent score includes a callperformance.
 18. The computer system of claim 15, wherein saiddisambiguation comprises: processing data indicative of how therelationships of finer grain values to coarser grain values changes overtime includes, for the particular coarser grain value, includesdetermining a set of finer grain values for dimension coordinates havingthe particular coarser grain value; determining a set of dimensioncoordinates having finer grain values in the determined set of finergrain values, including dimension coordinates having finer grain valuesin the determined set of finer grain values but not having theparticular coarser grain value.
 19. The computer system of claim 15,wherein said unfettering comprises: for the dimension coordinates,having the particular finer grain value, on which the determination ofperformance score for the particular finer grain value is based, atleast some of those dimension coordinates do not have the particularcoarser grain value.
 20. The computer system of claim 19, wherein:determining a set of dimension coordinates having finer grain values inthe determined set of finer grain values, including dimensioncoordinates having finer grain values in the determined set of finergrain values but not having the particular coarser grain value includes:processing a query constraint that is expressed in terms of theparticular coarser grain value to determine an initial set of dimensioncoordinates; and rewriting and reapplying the query constraint in termsof finer grain values of the determined initial set of dimensioncoordinates, to determine the set of dimension coordinates.
 21. Thecomputer system of claim 15, wherein: the particular finer grain valueis a first particular finer grain value of a plurality of particularfiner grain values; and the computer system is further configured to:process a temporal mode to determine a time extent descriptor; anddetermine the plurality of finer grain values based at least in part onprocessing the determined time extent descriptor.
 22. A method ofprocessing a performance query to a dimensionally-modeled factcollection by processing dimension coordinates that exist within adimensional data model having slowly changing dimensions, wherein factsof the dimensionally-modeled fact collection are accessible according tothe dimensional data model and wherein the dimension coordinates have afirst particular grain (“finer grain”) that is finer than a secondparticular grain (“coarser grain”), the method to determine anevaluative score for a particular value at the finer grain (“finer grainvalue”) based on performance facts for dimension coordinates associatedwith the particular finer grain value, the method comprising:determining at least one performance parameter, relative to a particularvalue at the coarser grain (“coarser grain value”), against which tomeasure the performance facts associated with the finer grain value,including processing temporal relationships of finer grain values tocoarser grain values for the dimension coordinates, wherein processingthe temporal relationships of finer grain values to coarser grain valuesincludes processing data indicative of how relationships of the finergrain values to coarser grain values change over time and, basedthereon, processing performance facts based on performance facts fordimension coordinates having the particular coarser grain value;determining the evaluative score for the particular finer grain valuebased on performance facts of dimension coordinates having theparticular finer grain value, in view of the at least one determinedperformance parameter; wherein processing the temporal relationships offiner grain values to coarser grain values includes performing at leastone of unfettering and disambiguation, and to account for a changingrelationship of grains so that the evaluative score is adjusted toprovide a different type of evaluation score than a score based on astatic relationship of grain values; said disambiguation comprisingremapping the correspondence of fine-grained entities to coarser-grainedentities when required to remove an ambiguity in how a fine grainedentity maps to a coarser grained entity; and said unfettering comprisingre-expressing the constraint in terms of finer-grained entities ofdimension coordinates satisfying an original, coarser-grained constraintwhen required to account for a time changing relationship between finergrains and coarser grains; and generating a report including at leastone statistical measure of performance based on the evaluative score.23. The method of claim 22, wherein the dimensionally-modeled factcollection is for an organization having individual employee agentsassigned to teams and the report includes a sales performance for teamsthat is different than for scoring based on a static relationship ofemployee agents to teams.
 24. The method of claim 22, wherein thedimensionally-modeled fact collection is for an organization havingindividual employee agents assigned to teams and the report includes asales performance for agents that is different than for scoring based ona static relationship of employee agents to teams.
 25. A computerprogram product for processing a performance query to adimensionally-modeled fact collection by processing dimensioncoordinates that exist within a dimensional data model having slowlychanging dimensions, wherein facts of the dimensionally-modeled factcollection are accessible according to the dimensional data model andwherein the dimension coordinates have a first particular grain (“finergrain”) that is finer than a second particular grain (“coarser grain”),the method to determine an evaluative score for a particular value atthe finer grain (“finer grain value”) based on performance facts fordimension coordinates associated with the particular finer grain value,the computer program product comprising at least one computer-readablemedium having computer program instructions stored therein which areoperable to cause at least one computing device to: determine at leastone performance parameter, relative to a particular value at the coarsergrain (“coarser grain value”), against which to measure the performancefacts associated with the finer grain value, including processingtemporal relationships of finer grain values to coarser grain values forthe dimension coordinates, wherein processing the temporal relationshipsof finer grain values to coarser grain values includes processing dataindicative of how relationships of the finer grain values to coarsergrain values change over time and, based thereon, processing performancefacts based on performance facts for dimension coordinates having theparticular coarser grain value; and determine the evaluative score forthe particular finer grain value based on performance facts of dimensioncoordinates having the particular finer grain value, in view of the atleast one determined performance parameter; wherein processing thetemporal relationships of finer grain values to coarser grain valuesincludes performing at least one of unfettering and disambiguation toaccount for a changing relationship of grains so that the evaluativescore is adjusted to provide a different type of evaluation score than ascore based on a static relationship of grain values; saiddisambiguation comprising remapping the correspondence of fine-grainedentities to coarser-grained entities when required to remove anambiguity in how a fine grained entity maps to a coarser grained entity;and said unfettering comprising re-expressing the constraint in terms offiner-grained entities of dimension coordinates satisfying an original,coarser-grained constraint when required to account for a time changingrelationship between finer grains and coarser grains; and generating areport including at least one statistical measure of performance basedon the at least one evaluative score.
 26. The computer program productof claim 25, wherein the dimensionally-modeled fact collection is for anorganization having individual employee agents assigned to teams and thereport includes a sales performance for teams that is different than forscoring based on a static relationship of employee agents to teams. 27.The computer program product of claim 25, wherein thedimensionally-modeled fact collection is for an organization havingindividual employee agents assigned to teams and the report includes asales performance for agents that is different than for scoring based ona static relationship of employee agents to teams.
 28. A computer systemconfigured to process a performance query to a dimensionally-modeledfact collection by processing dimension coordinates that exist within adimensional data model having slowly changing dimensions, wherein factsof the dimensionally-modeled fact collection s are accessible accordingto the dimensional data model and wherein the dimension coordinates havea first particular grain (“finer grain”) that is finer than a secondparticular grain (“coarser grain”), the method to determine anevaluative score for a particular value at the finer grain (“finer grainvalue”) based on performance facts for dimension coordinates associatedwith the particular finer grain value, the computer system configuredto: determine performance parameters, relative to a particular coarsergrain value, against which to measure the performance facts associatedwith the finer grain value, including processing the temporalrelationships of finer grain values to coarser grain values for thedimension coordinates; and determine the evaluative score for theparticular finer grain value based on performance facts of dimensioncoordinates having the particular finer grain value, in view of thedetermined performance parameters; wherein processing the temporalrelationships of finer grain values to coarser grain values includesperforming at least one of unfettering and disambiguation to account fora changing relationship of grains so that the evaluative score isadjusted to provide a different type of evaluation score than a scorebased on a static relationship of grain values; said disambiguationcomprising remapping the correspondence of fine-grained entities tocoarser-grained entities when required to remove an ambiguity in how afine grained entity maps to a coarser grained entity; and saidunfettering comprising re-expressing the constraint in terms offiner-grained entities of dimension coordinates satisfying an original,coarser-grained constraint when required to account for a time changingrelationship between finer grains and coarser grains; and generating areport including at least one statistical measure of performance basedon the at least one evaluative score.
 29. The system of claim 28,wherein the dimensionally-modeled fact collection is for an organizationhaving individual employee agents assigned to teams and the reportincludes a sales performance for teams that is different than forscoring based on a static relationship of employee agents to teams. 30.The system of claim 28, wherein the dimensionally-modeled factcollection is for an organization having individual employee agentsassigned to teams and the report includes a sales performance for agentsthat is different than for scoring based on a static relationship ofemployee agents to teams.